We can initially approach the concept of GDP by explaining the terms “Product”, “Domestic” and “Gross” separately (Lequiller and Blades 2014, Chapter 1):
Product: refers to what we are trying to measure, which is the production of goods and services, with no double counting, in a given period, \(t\) and carried out by:
Profit-making enterprises (economists use the alternative term firms)
Non-profit institutions
Goverment bodies
Households
“Domestic”: indicates that the production to be taken into account is the one that is carried within a certain geographical territory, \(s\), clearly delimited.
“Gross”: it means that depreciation or the cost of using capital is not deducted (in the field of economics it is called consumption of fixed capital).
In other words, the decrease in the value of the assets due to physical deterioration, foreseeable wear or accidental damage is not deducted.
Why depreciation or the cost of using capital is not deducted?
Initially the units in which GDP is measure is in monetary units of a specific currency, \(c\). Therefore \(GDP_{s}^{c}(t)\) means the \(GDP\) of territory \(s\) in a given period \(t\). To make the discussion less abstract we present a plot of \(GDP\) for Colombia, \(s = COL\), expressed in Colombian pesos, \(c = COP\), for the years 1960 to 2019, \(t = 1960, \ldots, 2019\):
# Clean data
gdp_colombia <- wbstats::wb(country = "COL",
indicator = "NY.GDP.MKTP.CN",
startdate = 1960,
enddate = 2019) %>%
tibble::as_tibble() %>%
dplyr::select(date, value) %>%
dplyr::mutate(date = as.double(date),
label_text = stringr::str_glue('Year: {date}
GDP: {value %>% scales::dollar()}'))
# Plot
static_plot <- gdp_colombia %>%
# Data
ggplot2::ggplot(aes(x = date, y =value)) +
# Geoms
ggplot2::geom_point(aes(text = label_text),
shape = 21,
color = "black",
fill = "red") +
ggplot2::geom_line(linetype = "dashed") +
# Scales
scale_x_continuous(breaks = c(1960:2019)) +
scale_y_continuous(breaks = seq(from = 0, to = 1.10e15, by = 1e14),
labels = scales::number_format(scale = 1/1e12, suffix = "B")) +
labs(x = "Year",
y = "GDP in current local currency [B = Billion in long scale (10^12)]",
title = "GDP of Colombia: 1960-2019") +
# Themes
theme(panel.border = element_rect(fill = NA, color = "black"),
plot.background = element_rect(fill = "#f3fcfc"),
panel.background = element_rect(fill = "#f3f7fc"),
legend.background = element_rect(fill = "#f3fcfc"),
plot.title = element_text(face = "bold"),
axis.text.x=element_text(angle = -90, vjust = 0.5),
axis.title = element_text(face = "bold"),
legend.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"))
# Interactivity
static_plot %>%
plotly::ggplotly(tooltip = "text")Lequiller, François, and Derek Blades. 2014. Understanding National Accounts: Second Edition. OECD. https://doi.org/10.1787/9789264214637-en.